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Type : Classification
User : lmn
Analysis name: my-cclassificatio-demo_Auto select best model_20200529
Model selection : Auto select best model
Date time : 2020-05-29 14:35:36.111660
Input data: credit

	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_1	PAY_2	PAY_3	PAY_4	PAY_5	...	BILL_AMT4	BILL_AMT5	BILL_AMT6	PAY_AMT1	PAY_AMT2	PAY_AMT3	PAY_AMT4	PAY_AMT5	PAY_AMT6	default
0	20000	2	2	1	24	2	2	-1	-1	-2	...	0.0	0.0	0.0	0.0	689.0	0.0	0.0	0.0	0.0	1
1	90000	2	2	2	34	0	0	0	0	0	...	14331.0	14948.0	15549.0	1518.0	1500.0	1000.0	1000.0	1000.0	5000.0	0
2	50000	2	2	1	37	0	0	0	0	0	...	28314.0	28959.0	29547.0	2000.0	2019.0	1200.0	1100.0	1069.0	1000.0	0
3	50000	1	2	1	57	-1	0	-1	0	0	...	20940.0	19146.0	19131.0	2000.0	36681.0	10000.0	9000.0	689.0	679.0	0
4	50000	1	1	2	37	0	0	0	0	0	...	19394.0	19619.0	20024.0	2500.0	1815.0	657.0	1000.0	1000.0	800.0	0

5 rows × 24 columns

(24000, 24)

Data for Modeling: (22800, 24)
Unseen Data For Predictions: (1200, 24)

Preproccessing and EDA results

Models performance

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa
0	Ridge Classifier	0.82	0	0.3524	0.6806	0.4641	0.3688
1	Linear Discriminant Analysis	0.8198	0.7625	0.3686	0.6685	0.4749	0.3769
2	Light Gradient Boosting Machine	0.8193	0.7717	0.3703	0.666	0.4754	0.3769
3	Extreme Gradient Boosting	0.819	0.7812	0.3524	0.6755	0.4626	0.3665
4	Gradient Boosting Classifier	0.8187	0.7808	0.3578	0.6696	0.4659	0.3686
5	CatBoost Classifier	0.8182	0.7738	0.3691	0.6614	0.4732	0.3738
6	Ada Boost Classifier	0.8173	0.77	0.3329	0.6774	0.446	0.3517
7	Extra Trees Classifier	0.8047	0.7375	0.364	0.5959	0.4515	0.3412
8	Random Forest Classifier	0.8016	0.7212	0.3119	0.5994	0.4097	0.3047
9	Logistic Regression	0.7784	0.6496	0.0008	0.25	0.0017	0.0001
10	K Neighbors Classifier	0.7514	0.6027	0.1762	0.3706	0.2386	0.112
11	Decision Tree Classifier	0.7211	0.6102	0.4108	0.3801	0.3946	0.2139
12	SVM - Linear Kernel	0.6816	0	0.228	0.2121	0.1727	0.0416
13	Quadratic Discriminant Analysis	0.6267	0.7319	0.5782	0.4422	0.397	0.2093
14	Naive Bayes	0.3496	0.6377	0.9119	0.2423	0.3828	0.0512

Best suggested model based on best value for Ridge Classifier is Accuracy
Selected Ridge Classifier based on Accuracy

Selected models training results & plots

	Accuracy	AUC	Recall	Prec.	F1	Kappa
0	0.8158	0.0	0.3456	0.6595	0.4535	0.3555
1	0.8252	0.0	0.3654	0.7011	0.4804	0.3876
2	0.8283	0.0	0.3768	0.7112	0.4926	0.4008
3	0.8308	0.0	0.3711	0.7318	0.4925	0.4037
4	0.8246	0.0	0.3881	0.6816	0.4946	0.3980
5	0.8139	0.0	0.3399	0.6522	0.4469	0.3481
6	0.8252	0.0	0.3343	0.7284	0.4583	0.3707
7	0.8127	0.0	0.3399	0.6452	0.4453	0.3453
8	0.8202	0.0	0.3428	0.6875	0.4575	0.3638
9	0.8100	0.0	0.3399	0.6316	0.4420	0.3397
Mean	0.8207	0.0	0.3544	0.6830	0.4664	0.3713
SD	0.0068	0.0	0.0181	0.0334	0.0202	0.0233

Completed trained with default hyperparameters tuned RidgeClassifier(alpha=0.105, class_weight=None, copy_X=True,
                fit_intercept=False, max_iter=None, normalize=False,
                random_state=3286, solver='auto', tol=0.001)

	Parameters
alpha	0.105
class_weight	None
copy_X	True
fit_intercept	False
max_iter	None
normalize	False
random_state	3286
solver	auto
tol	0.001

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa
0	Ridge Classifier	0.8256	0	0.3642	0.7046	0.4802	0.3879

	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_1	PAY_2	PAY_3	PAY_4	PAY_5	...	BILL_AMT5	BILL_AMT6	PAY_AMT1	PAY_AMT2	PAY_AMT3	PAY_AMT4	PAY_AMT5	PAY_AMT6	default	Label
0	50000	2	2	1	48	0	0	0	0	0	...	7988.0	8011.0	2028.0	2453.0	2329.0	431.0	300.0	500.0	0	0
1	200000	2	1	1	40	2	2	2	2	2	...	87003.0	89112.0	4200.0	4100.0	3000.0	3400.0	3500.0	0.0	1	1
2	50000	2	3	1	44	1	2	3	2	4	...	16341.0	15798.0	2100.0	1000.0	2300.0	0.0	0.0	0.0	1	1
3	60000	2	2	1	31	2	2	-1	0	0	...	31232.0	30384.0	1132.0	60994.0	1436.0	1047.0	1056.0	1053.0	1	1
4	120000	2	3	2	32	-1	0	0	0	0	...	79589.0	81354.0	2429.0	3120.0	3300.0	10000.0	3200.0	3200.0	0	0

5 rows × 25 columns

Pickling model to lmn/my-cclassificatio-demo/Auto select best model/outputs/2020-05-29 14:35:36.111660/
Transformation Pipeline and Model Succesfully Saved